Synergistic Effect of Dual-Doped Carbon on Mo2C Nanocrystals Facilitates Alkaline Hydrogen Evolution
Bibliographic record
Abstract
Abstract Molybdenum carbide (Mo 2 C) materials are promising electrocatalysts with potential applications in hydrogen evolution reaction (HER) due to low cost and Pt-like electronic structures. Nevertheless, their HER activity is usually hindered by the strong hydrogen binding energy. Moreover, the lack of water-cleaving sites makes it difficult for the catalysts to work in alkaline solutions. Here, we designed and synthesized a B and N dual-doped carbon layer that encapsulated on Mo 2 C nanocrystals (Mo 2 C@BNC) for accelerating HER under alkaline condition. The electronic interactions between the Mo 2 C nanocrystals and the multiple-doped carbon layer endow a near-zero H adsorption Gibbs free energy on the defective C atoms over the carbon shell. Meanwhile, the introduced B atoms afford optimal H 2 O adsorption sites for the water-cleaving step. Accordingly, the dual-doped Mo 2 C catalyst with synergistic effect of non-metal sites delivers superior HER performances of a low overpotential (99 mV@10 mA cm −2 ) and a small Tafel slope (58.1 mV dec −1 ) in 1 M KOH solution. Furthermore, it presents a remarkable activity that outperforming the commercial 10% Pt/C catalyst at large current density, demonstrating its applicability in industrial water splitting. This study provides a reasonable design strategy towards noble-metal-free HER catalysts with high activity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".